NDLI: Population coding of somatic sensations

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Year: 2011, Volume: 27

Year: 2010, Volume: 26

Population coding of somatic sensations

Content Provider	PubMed Central
Author	Ma, Qiufu
Copyright Year	2012
Abstract	The somatic sensory system includes a variety of sensory modalities, such as touch, pain, itch, and temperature sensitivity. The coding of these modalities appears to be best explained by the population-coding theory, which is composed of the following features. First, an individual somatic sensory afferent is connected with a specific neural circuit or network (for simplicity, a sensory-labeled line), whose isolated activation is sufficient to generate one specific sensation under normal conditions. Second, labeled lines are interconnected through local excitatory and inhibitory interneurons. As a result, activation of one labeled line could modulate, or provide gate control of, another labeled line. Third, most sensory fibers are polymodal, such that a given stimulus placed onto the skin often activates two or multiple sensory-labeled lines; crosstalk among them is needed to generate one dominant sensation. Fourth and under pathological conditions, a disruption of the antagonistic interaction among labeled lines could open normally masked neuronal pathways, and allow a given sensory stimulus to evoke a new sensation, such as pain evoked by innocuous mechanical or thermal stimuli and itch evoked by painful stimuli. As a result of this, some sensory fibers operate along distinct labeled lines under normal versus pathological conditions. Thus, a better understanding of the neural network underlying labeled line crosstalk may provide new strategies to treat chronic pain and itch.
Related Links	http://dx.doi.org/10.1007/s12264-012-1201-2
Ending Page	99
Page Count	9
Starting Page	91
File Format	PDF
ISSN	16737067
e-ISSN	19958218
Journal	Neuroscience bulletin
Issue Number	2
Volume Number	28
Language	English
Publisher Date	2012-04-01
Access Restriction	Open
Subject Keyword	Physiology Neuroscience(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Neuroscience Physiology Medicine

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